OP.GG, Mobalytics, U.GG, Blitz.gg, Porofessor and Sinergia — every League of Legends ranked player has used at least two of them. But if you queue ADC + Support as a premade, none of the big five was built for you. They analyze two players. Your bot lane is one duo. This guide compares all six tools across the metrics that actually decide whether your botlane wins or loses, and explains why duo-as-unit analysis is the missing layer in every popular LoL analytics stack.
You and your duo partner just lost three lanes in a row. You both finished with a 5/3/8 KDA. Vision was decent. Damage was acceptable. You open OP.GG. You open Mobalytics. Both tell you that your individual stats are fine. Yet your duo lost. The argument starts: "You should have followed my engage." "You called for a fight I couldn't reach." Two healthy stat lines, one broken lane, zero data to settle the question.
This is the structural blind spot of every major League of Legends analytics tool in 2026. They were built for the solo player who climbs alone. The metrics, dashboards and coaching layers all assume an individual unit of analysis. A premade duo is not an individual. It is a coordinated micro-unit operating under shared assumptions, shared timings and shared decisions. When that coordination breaks, no single-player metric will show it — both players' individual cards still look fine.
CoreJJ's framework, repeated across his Aimlabs interviews and InvenGlobal coaching, makes this explicit: the bot lane is not won by mechanical skill alone. It is won by shared understanding — knowing when your duo will engage before they say it, when to recall together, when to commit to a 2v2 trade. These behaviors are real, measurable in the Riot timeline, and yet none of the standard tools surface them. Grabbz (Head Coach, Fnatic) and Caedrel echo the same: G2's identity was built around the bot lane precisely because it commands map vision for dragons and rift herald — and that command is a duo property, not an individual one.
When you search for "duo analysis" in any incumbent tool, you'll find one of these three things — and none of them actually measures your specific pair:
In not a single one of those cases is the most basic question of duo analysis answered: how much better or worse do you play together than apart? That question has remained structurally unanswered until very recently.
If "two individual reports next to each other" is not duo analysis, what is? The honest answer borrows from disciplines that solved this problem decades ago in other sports. Three traditions matter for League of Legends:
Developed by Wayne Winston and Jeff Sagarin, Adjusted Plus-Minus (APM) measures the net impact of a player on the score while on the court, controlling for the quality of teammates and rivals. Translated to LoL: the net impact of your partner's presence on your measured performance. A player whose KDA jumps from 2.1 solo to 4.8 when queued with a specific duo partner is producing a measurable interaction effect. That effect is the seed of every modern duo-coordination metric.
Sarah Rudd's work on Markov-chain possession models in football, and shot-quality analyses in hockey, established the principle that the performance of a tactical unit cannot be inferred directly from the individual statistics of its components. A central midfielder may show poor passing accuracy because they're operating in dense traffic — a duo-aware metric corrects for that context. Apply this to bot lane: a Support's "low damage" is misleading if they're a Janna whose role is to keep the ADC alive. Context-corrected metrics are duo-aware metrics.
Mobalytics proved that it is possible to aggregate heterogeneous individual performance metrics into a single score that non-technical players can understand. The Gaming Performance Index (GPI) became the de facto consumer standard for an individual skill radar — Farming, Vision, Aggression, Fighting, Survivability, Objectives, Consistency, Versatility. It's a remarkable achievement at the individual level. It is also, by design, exactly the wrong unit of analysis for a premade duo.
From these three traditions, six metric families emerge as the genuine coordination signals — none of which appear on a standard OP.GG or Mobalytics page:
These six families form the spine of the Duo Performance Index (DPI v1.1), Sinergia's open methodology — and as we'll see, they are precisely what no incumbent tool measures.
Tool reviews on the internet are typically generic listicles that score "best for solo queue" or "best free overlay." For a botlane premade, none of those criteria are what matters. We evaluated each of the six tools against seven duo-specific axes, scored on a four-level scale (✓ Full, ◐ Partial, ◌ Marginal, ✗ Absent):
All six tools are unofficial and not endorsed by Riot Games. Traffic figures cited below are approximate estimates from public web-analytics platforms and fluctuate over time. We have no commercial relationship with OP.GG, Mobalytics, U.GG, Blitz.gg, Porofessor or LoLalytics — though we openly note that Sinergia is the product being positioned in this comparison.
OP.GG is the largest LoL analytics property by traffic by an order of magnitude. The scale alone makes it indispensable for quick lookups — match history, KDA snapshots, ranked progression, champion mastery, win-rate-by-champion. For a fast scouting check on a champ-select rival or a friend's recent slump, nothing beats the speed.
OP.GG's Multi-Search lets you query several summoners simultaneously — a champ-select staple for premade-friendly scouting. Its Desktop App added a Rival feature in mid-2025, which compares two friends' games, win rate and tier graph side by side over time. The scale also means OP.GG is the most up-to-date source for global per-champion win rates, runes and item builds patch-over-patch.
The gap is structural. OP.GG operates a separate subdomain — duo.op.gg — but it is a duo-finding LFG service, not a duo-performance analyzer. You filter by tier, position, recent stats and preferred champions to find a partner; you do not analyze how your existing partner and you perform as a unit. The Multi-Search and Rival features remain comparative, never synergistic. There is no joint match history view, no shared metric, no coordination score.
If you want OP.GG to tell you "your duo's CS@14 differential was −12 in your last 20 games" or "your synergy with Leona+Draven is +6% above your individual baselines," you'll be waiting. That layer doesn't exist in the product, and OP.GG's strategic priority in 2025–2026 appears to be esports broadcasting integrations rather than premade coordination tooling.
Mobalytics is the most ambitious consumer-grade coaching platform in League of Legends. Its Gamer Performance Index (GPI) — an eight-dimension skill radar — has been the de facto consumer standard for individual improvement since 2017 and remains the benchmark for solo climbers. Mobalytics was acquired by ESL FACEIT Group in March 2025, formally consolidating its position in the esports analytics ecosystem.
The GPI is genuinely excellent at what it measures: individual skill across Farming, Vision, Aggression, Fighting, Survivability, Power Play, Consistency and Versatility, with role-weighted scoring. It takes roughly 20 games per role to stabilize, and the resulting radar gives a non-technical player a coherent picture of their strengths and weak axes. The patch-aware tier lists, build pages and pre-game pages are polished. Mobalytics' self-reported claim is that players improve climb speed by 27% using the tool (this figure is auto-reported, not peer-reviewed — take with appropriate context).
The GPI is per-player by design. There is no joint dimension. Vision is scored from the support's perspective or the ADC's, not from the duo's combined vision footprint. Aggression is calibrated to your role, not to your shared decision to engage as a pair. Mobalytics' bot-lane content is editorial — "Best Bot Lane Duos in Season 2026"–style blog posts based on aggregate champion-pair win rates, not on your specific games.
Post-acquisition, the public direction signals further alignment with EFG's tournament and esports infrastructure: pro-team partnerships, integrations with FACEIT competitive ladders, esports broadcasting overlays. None of that is bad — it just isn't a roadmap toward premade-duo coordination analytics. As of May 2026 there is no announced GPI extension for pairs.
U.GG is Riot-approved, fast, and increasingly the default reference for global per-champion win rates and patch-level meta calls. It operates a public Duo Tier List covering both bot-lane and jungle-mid pairings. For deciding which champion combination to first-pick in your duo lobby, U.GG is the most consulted source.
The Duo Tier List explicitly ranks ADC + Support pairings by aggregate win rate, sample size, and "synergy score" delta vs. the sum of the two champions' individual baselines. It surfaces the meta combos every patch — Caitlyn + Nami, Draven + Senna, Aphelios + Renata, and so on — and adapts to elo tiers. The overlay app for runes and builds is competitive with OP.GG and Blitz, and U.GG's developer-facing API exposure makes it a frequent backend for other tools.
The Duo Tier List is aggregate, not personal. "Caitlyn + Nami has +3.7% synergy" is computed across 100k+ games globally. It tells you nothing about whether you and your friend execute that combo well. A specific duo can be 35% WR on a globally S-tier combination because their style doesn't match it. Aggregate tier lists are a useful general reference; they are not duo analysis.
U.GG also has no joint-history view. You can search yourself and your partner separately and read two parallel pages. There is no metric that aggregates your shared games into a coherent pair profile.
Blitz.gg is the most user-friendly in-game overlay in the LoL ecosystem. The product is a productivity layer for the individual player: it imports builds and runes automatically, surfaces jungle timers, tracks ARAM augments and ultimate cooldowns on enemy portraits, and offers item-value comparisons mid-game. It is a beloved utility — and a completely individual one.
Speed. The friction-free build importer alone justifies the install. Blitz removes the cognitive load of remembering which runes to pick on a flex champion mid-pool. Its overlay is light, polished, and reliable across regions.
There is no duo-specific surface anywhere in the product. No joint history, no shared coordination metric, no premade-aware coaching layer, no champion-combo recommendations grounded in your games. The support overlay is gated behind a Premium subscription. Blitz is best understood as a single-player productivity layer that you and your duo partner happen to both install — not as a tool that analyzes the two of you together.
Porofessor's strength is the live lobby. The moment your match starts, it surfaces every player's recent form, mains, lane matchup win rates, and synergy/counter flags pulled from League of Graphs data. The customizable overlay is genuinely useful for in-game decision-making.
Where it shines is the per-player snapshot during champ select and loading screen: CS@10, CS@20, gold@10, gold@20, ward placement frequency, KDA, lane matchup historical win rate. The synergy/counter labels include some pair flags (e.g., "this player has queued with this teammate 12 times before") — but they exist as tags, not as a deep analytical view.
A pair-flag at lobby time is not a synergy analysis. There is no metric, score, or longitudinal view of your duo's coordination quality. Porofessor never asks "across your 25 shared games, how is your CS differential trending?" — it asks "in this specific lobby, who has the higher recent KDA?" Useful, but a different question.
LoLalytics is the deepest pure-stats site in the ecosystem. Its champion-pair synergy data is the most granular you'll find — sample-size transparent, patch-aware, tier-filtered. For draft analysts and meta theorists, it's invaluable.
The site exposes raw aggregate data on champion vs. champion matchups and champion-pair synergies with statistical rigor. "Caitlyn + Lulu: 51.4% WR across 118,237 games on Patch 26.10" is the kind of figure you can act on. The interface is dense but the data is honest.
Same structural gap as U.GG, just more rigorous. Champion-pair stats are not player-pair stats. The 118k games behind "Caitlyn + Lulu" come from any duo in the world. Your duo's performance on Caitlyn + Lulu may be radically different and the only way to know is to compute on your shared history. LoLalytics does not do that.
Every tool reviewed above does something well. None of them analyzes the duo as a single unit, because they weren't built for that question. Sinergia is purpose-built for it — and is intentionally designed to complement, not replace, the existing stack. Use OP.GG for fast individual lookups. Use Mobalytics for solo GPI tracking. Use Porofessor for live lobbies. Use Sinergia for the dedicated post-session audit of your duo.
Sinergia ingests your shared match history via the Riot Games API — up to 50 ranked games (Solo/Duo and Flex, remakes excluded) where one of you played UTILITY/SUPPORT and the other played BOTTOM on the same team, telling you how many games it analysed and how many it dropped — and produces a guided analysis built around a published methodology, the Duo Performance Index (DPI v1.1). The DPI is a composite 0–100 score that quantifies how the two of you perform as one unit.
Unlike every other consumer LoL analytics tool — none of which publishes its scoring methodology — Sinergia publishes the full DPI formula. The five dimensions and their weights:
| Dimension | Weight | What it measures |
|---|---|---|
| Individual gameplay | 45% | Average of support and ADC scores across 5 role-specific metrics: vision, control wards, survival, KDA, and impact. |
| Shared winrate | 25% | Actual win percentage in shared ranked games. Counted once to avoid double-weighting with individual metrics. |
| Lane phase @14 | 15% | CS differential and botlane gold differential at minute 14 — the exact moment of first recall and pro-standard snapshot. |
| Lane proximity | 10% | Percentage of time the duo moves and fights together between minutes 2 and 14, measured from Riot timeline frame positions. |
| Jungle pressure | 5% | Whether your jungler prioritizes bot when the duo creates pressure. Measured as gank differential in botlane vs. rival botlane. |
The aggregation is straightforward and reproducible:
| DPI | Interpretation | Recommended action |
|---|---|---|
| 85–100 | Exceptional synergy | Keep current combo and champion pool |
| 70–84 | Solid synergy | Optimize weak combos, keep the strong ones |
| 55–69 | Positive synergy | Identify the weakest dimension and work on it |
| 45–54 | Expected performance | Evaluate whether the duo adds value or is neutral |
| 30–44 | Mild negative synergy | Review coordination and combo selection |
| 0–29 | Significant negative synergy | Analyze systemic tilt or style incompatibility |
Once the DPI is computed, Sinergia runs a second pass over your shared game data and generates a full analytical report in plain language — the Coach IA. This is the section most users describe as genuinely surprising, because it doesn't read like a dashboard. It reads like someone who watched your games. Here's exactly what it produces and why each piece is structurally different from anything the incumbent tools output.
The coach opens with a direct winrate verdict calibrated to tone — four different phrasings depending on whether you're at 65%+, 55-64%, 45-54%, or below 45%. It immediately follows with a recent form overlay: if your last 5 games show 4+ wins the coach flags a hot streak and tells you not to break the momentum by changing picks. If you're 1W-4L in the last 5, it flags a recent breakdown and recommends a mental reset before the next session. No other tool surfaces the delta between your season average and your current trajectory in the same breath.
The coach computes a point-biserial correlation between your dragon differential and win/loss across your shared games. If r=0.62, it says so. It then shows your average drag vs. the rival's, the conditional win rate when you have more dragons, and one specific executable action: "Ward dragon at 4:30 without exception." If your team is losing dragon soul battles — 3 rival stacks vs. your 1 — the coach explicitly states that the fourth dragon is worth more than a Baron and you should sacrifice map pressure to contest it. This is not generic advice. It's derived from the correlation in your games.
If timeline data is available, the coach computes your average plates vs. the rival's and converts the differential into exact gold: each plate is 125g local gold, so if the rival averages +1.8 plates per game, that's 225g the rival carries over you before minute 14 every single game. No other tool in the comparison set surfaces plate differentials. The coach pairs this with a structural rule — push wave before any rotation — that addresses the root cause, not just the symptom. ADC turret damage output is also compared: if the rival ADC deals significantly more damage to structures, the coach calls it out explicitly.
Each major objective is analyzed individually — average units secured vs. rival, conditional win rate when you hold it, and a specific rotational rule. On Herald: if you're losing it, the coach anchors the window at 14-16 minutes and explains that executing the play requires lane priority built in advance. On Baron: a fixed rule is surfaced ("bot lane always attends Baron — the ADC deals the most DPS to the objective") backed by your specific WR in games where you secured Baron. On Grubs: the coach explains the structure-damage buff mechanic and whether shove-and-contest is viable based on your early lane priority pattern.
This is probably the most novel metric in the entire output. Using Riot timeline frame data, Sinergia detects whether both players were within fight range of each dragon and Baron fight in your shared games. It then computes two conditional win rates: WR when both of you are present at the objective vs. WR when one of you is absent. In some duos this gap is 20+ percentage points — meaning your single biggest win-lever isn't mechanics, it's map rotation timing. The coach surfaces this as a percentage (e.g. "67% presence in objective fights") with the conditional WR split and a concrete instruction: push wave 30 seconds before each objective spawn timer.
The coach uses data at the 14-minute snapshot when available (the pro-standard first-recall checkpoint) rather than end-game totals, which are distorted by how long the game lasted. It surfaces CS differential, botlane gold differential, and XP differential all at minute 14 — and compares each against rank-specific benchmarks. The benchmark table runs from Silver through Diamond: Gold average for CSD@14 is 0, Platinum is +3, Emerald is +5. If you're sitting at -12 in Gold, the coach doesn't just say you're behind — it says exactly how far below the tier average you are and explains the mechanical fix (freeze near your tower when you have wave advantage).
Win rates segmented into three buckets: games under 25 minutes, 25–35 minutes, and over 35 minutes. The coach identifies the game length where you do best and translates it into champion selection guidance. If you do best in short games and bring Jinx and Vayne, you are fighting your own profile; if you do best in long games and run full engage into a poke comp, same thing. Game length also depends on how the game goes, so treat it as a hint, not a label. The advice is pick-specific and actionable for the next champion select.
The coach computes ADC damage share against a rank-specific benchmark (Gold: 24%, Platinum: 26%, Emerald: 28%) and separates the cause of low numbers: is the ADC dying before dealing damage (deaths-driven), or is the ADC arriving late to fights (KP-driven)? Kill participation for both players is shown against tier benchmarks — Gold reference is 62% for the support and 57% for the ADC. Support CC seconds per game rounds out the picture: a Leona averaging 8 CC seconds in Gold-level games is underperforming; a Soraka at 4 CC seconds might be doing exactly her job. The interpretation adjusts by archetype.
Using Riot timeline event positions, the coach counts kills and assists the support was involved in outside the bot lane quadrant for the full game duration. This is a roaming impact score that no incumbent tool surfaces. A support averaging under 0.8 roam participations per game is rarely leaving lane. The coach then computes win rate in high-roaming games vs. low-roaming games to determine whether roaming is a net positive for this particular duo — because for some support-ADC combinations, roaming leaves the ADC stranded and actually reduces win rate.
The coach closes with a single Priority #1 block, computed by ranking all identified issues by their correlation coefficient with win/loss in your games. It doesn't give you seven things to fix. It tells you the one with the highest r-value — whether that's dragon control (r=0.54), botlane gold differential (r=0.48), or vision differential (r=0.41) — and gives exactly one executable instruction. The reason this matters: most coaching advice fails because it's a list. Players process one priority. The coach is designed around that constraint.
A score without a recommended action is an academic data point. Sinergia's Coach Suggest translates the DPI into a specific champion combo recommendation for your next session, with explicit reasoning and two actionable tips. The system operates on four priority tiers:
Sinergia's Performance Picks are not based on champion win rate. They rank each player's champions by their real win–loss balance over that player's own ranked history. Riot does not publish LP per game, so instead of estimating LP the tool counts what actually happened:
Why balance and not win rate? A champion with 80% WR over 5 games (4–1, balance +3) ranks below one with 60% WR over 40 games (24–16, balance +8). The balance penalizes small samples and rewards consistency: a lucky short streak can't top the list, and every number is real data rather than an LP estimate.
Each duo-pair analysis opens as a ten-step guided tour, followed by eleven deeper views under More analysis — all Riot-API-derived, all processed client-side in your browser. No signup; only the requests to Riot go through our proxy, which caches matches (details in the legal notice). The breakdown:
Several of these don't exist anywhere else in the consumer LoL ecosystem. The session plan (02) sets one weekly objective, the metric where your duo trails the rival pair the most, with a concrete drill, and measures it only on games played after you set it; plans are saved in your browser. Duo vs rival (03) compares Riot match challenges and pings (control wards, wards cleared before minute 20, pick kills with an ally, enemy-missing, retreat and on-my-way pings, max CS lead, time dead…) game by game against the rival bot lane, with a paired statistical test that separates clear gaps from noise. Tilt detection (08) looks at the game you play right after two losses in a row in the same session and compares your duo's win rate, deaths, vision and CS in those games against your normal performance. If your win rate drops under tilt, the system surfaces it — and bundles a four-rule anti-tilt protocol you can apply as a duo. Winrate by advantage (J · Stats) answers questions like "when you win CS diff, what's your win rate?" using a real point-biserial correlation computed on your shared games — the same statistical method used in the 2022 Jadowski & Cunningham study published in Springer on 882 professional matches.
A coach companion walks you through the tour, commenting each section with sentences built from your own duo's numbers — a side panel on desktop, a compact dock on mobile. Game length now lives under More analysis as plain winrate by game length (under 25, 25–35 and over 35 minutes), without stamping the duo as an "early" or "late" game pair.
Sinergia processes every analysis client-side in your browser using the Riot Games API. No login. No subscription. No usage limits. No data collection. From the published whitepaper manifesto: "data belongs to those who generate it. Sinergia's analyses belong to the players who request them. We do not sell data to third parties. We do not use your history without explicit consent."
The seven duo-specific axes from our methodology, scored across all six tools. Green check (✓) = full coverage; amber dot (◐) = partial; red cross (✗) = absent. Sources: each tool's public product surface, documentation and patch notes as of May 2026.
| Feature | Sinergia | OP.GG | Mobalytics | U.GG | Blitz | Porofessor | LoLalytics |
|---|---|---|---|---|---|---|---|
| Duo-as-unit score (DPI-style) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Joint match history aggregation | ✓ | side-by-side | ✗ | ✗ | ✗ | lobby flag | ✗ |
| Lane phase @14 metrics | ✓ | @10 only | ✓ | basic | ✗ | @10/@20 | ✗ |
| Lane proximity tracking | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Personalized combo (your history) | ✓ | ✗ | solo | ✗ | ✗ | ✗ | ✗ |
| Objective fight presence (duo) | ✓ | ✗ | solo Power Play | ✗ | ✗ | ✗ | ✗ |
| Coordination-aware coaching | ✓ | ✗ | solo | ✗ | ✗ | ✗ | ✗ |
| Published methodology | ✓ DPI whitepaper | ✗ | GPI blog | ✗ | ✗ | ✗ | stats only |
| Tilt detection | ✓ | ✗ | Consistency | ✗ | ✗ | on rivals | ✗ |
| Free, no signup, no limits | ✓ | ✓ | freemium | ✗ | ✗ | ✗ | ✗ |
The pattern is consistent: every incumbent excels at the layer they were designed for — and every incumbent has a structural blind spot on duo-as-unit analysis. Sinergia is positioned in that gap, not as a competitor to OP.GG or Mobalytics, but as the dedicated audit layer your duo doesn't currently have.
No analytics methodology is complete, and the DPI v1.1 is explicit about its current limits. Intellectual honesty matters more than a perfect marketing surface — and players reading this guide deserve to know exactly where the methodology is approximation rather than ground truth.
None of these limits are dealbreakers for the intended use case — they are the kind of disclosures any honest analytics methodology should publish. Sinergia's full whitepaper at sinergia.tato9689.com/whitepaper goes into deeper detail and cites the methodological references (Winston & Sagarin's APM; Rudd's Markov-chain possession models; Mobalytics' own GPI introduction; Riot Games' 2021 competitive-integrity post).
A 10-minute method, repeatable after every 5–10 ranked games. The point isn't to score yourselves; the point is to identify the single lever for your next session and act on it. Improvement is measured in monthly trends, not single-game snapshots.
Open Sinergia. Enter both Riot IDs (Name#TAG format), pick the correct region (EUW, NA, EUNE, LAN, KR, BR), and select Botlane mode. The system detects matches where one of you played UTILITY/SUPPORT and the other played BOTTOM on the same team. Solo/Duo and Flex games both count, remakes are excluded, and the tool tells you how many games it analysed and how many it dropped. Start with a 20-game quick analysis; promote to 50 once you've validated the workflow.
The Duo Performance Index is the headline number on the analysis page. Match it to the interpretation table:
Scroll to the radar charts and look at the five DPI inputs: individual gameplay, shared winrate, lane phase @14, lane proximity, jungle pressure. Pick the single lowest. That is your lever for the next session.
Sinergia's Coach Suggest returns a specific champion combo for the next session, grounded in your data. Play that combo. Not the global S-tier combo, not your comfort combo — the one Sinergia recommends, which is anchored in your real win–loss balance with each champion and combo plus confirmed meta synergy where available.
Pull the analysis again. Compare DPI vs. the previous reading. Compare the targeted dimension specifically. Improvement is measured in trends, not snapshots. A 5-point DPI gain over a month is real synergy growth. A 5-point gain over a single session is variance. If you set a session plan, its objective is measured only on the games you played after setting it.
None of the six tools is universally "best." Each has a use case it owns. Match your situation to the recommendation:
The mature answer for most botlane premades is use them together. OP.GG for the 30-second scout. Mobalytics for the weekly solo-GPI tracking. Porofessor running during the game. Sinergia for the dedicated post-session duo audit. They serve different layers of the same improvement problem.
The DPI v1.1 is a functional approximation of a hard problem, not its definitive solution. The published roadmap is explicit about where the methodology goes next:
Full granularity from the Riot timeline endpoint. Productive support roams — roams that result in kills or assists in other lanes within a 60-second window. Intra-game tilt detection — deaths in the 60 seconds following a previous death; dives without vision post-mortem. Damage share and CC score metrics that distinguish an engage support who performs from one who feeds.
A machine-learning model trained on Sinergia's growing corpus of analyzed duos. As data from thousands of real pairs accumulates, weights will be learned automatically instead of fixed by hand. The hypothesis: a Diamond duo and a Bronze duo do not have the same predictors of synergy, and the DPI should reflect that.
October 2026 update: part of this roadmap is already live. The jungle + mid analysis is available, and there is also a personal coach for all five roles (top, jungle, mid, ADC and support): death map, lane against your direct opponent, ability combos, the minutes of each game worth a replay and a measurable plan with your progress. Free, no signup.
Bot lane is the most codependent duo in the game, but it isn't the only one. The roadmap (Bloque C.5) covers the three other premade duos LoL players queue with friends:
Each duo type will get its own bespoke metric extractors — not a copy of the botlane model. The longer-horizon target is the same framework applied to amateur-team analysis (Bloque D of the roadmap), with Riot Tournament API integration enabling automated league analytics for amateur clubs and tournaments.
Enter both Riot IDs below. Free. No signup. Up to 50 shared ranked games analyzed in ~1–3 minutes, all processed in your browser. The first time you see your DPI is the moment your duo improvement gets a number.